My super title

Plotting Covid-19 Cases according to Zipcodes using Bokeh

import tabula
import requests
import datetime
import geopandas as gpd
import pandas as pd
import json
from dateutil import parser
from bokeh.io import reset_output, output_notebook, show

from bokeh.plotting import figure
from bokeh.models import Div, Column, Row


#make bokeh output to notebook
reset_output()
output_notebook()

url = 'https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/Epidemiology/COVID-19%20Summary%20of%20Cases%20by%20Zip%20Code.pdf'
pdf= requests.get(url)
with open(f'covid19_in_sd_{datetime.datetime.now().date()}.pdf','wb') as f:
    f.write(pdf.content)

!ls *.pdf

covid19_in_sd_2020-04-18.pdf  covid19_in_sd_2020-04-20.pdf

covid19_in_sd_2020-04-19.pdf  covid19_in_sd_2020-04-21.pdf

def tabula_convert_pdf_to_df(pdf):

    raw_data = tabula.read_pdf(pdf,stream=True,pages=1)[0]
    title = tabula.read_pdf(pdf,pages=1)[0].columns[0].split('\r')[-1]

    dates = []
    for _ in title.split():
        try:
            dates.append(parser.parse(_, fuzzy=True))
        except Exception as e:
            pass

    date = dates[0] # no use now

    updated_time = datetime.datetime.combine(dates[1],datetime.time(dates[2].hour)) # no use now

    df= pd.DataFrame({
                      'ZipCode' : pd.concat([raw_data['Zip Code'].astype(str),
                                             raw_data['Zip Code.1'].astype(str)]),

                       'CaseCount': pd.concat([raw_data['Unnamed: 0'],raw_data['Count.1']
                                                     ])
                     })[:-2]

    df['CaseCount'] = df['CaseCount'].astype('int16')
    total_count = sum(df['CaseCount'])
    return title,total_count, df


title, total_count, count_data = tabula_convert_pdf_to_df('covid19_in_sd_2020-04-21.pdf')

county_gpd = gpd.read_file(f'Sandiego_Zip_codes.geojson')
county_gpd.head()

merged =county_gpd.merge(count_data, right_on = 'ZipCode', left_on = 'zip',
                         how = 'right').drop(columns=['zip']).rename(columns={'community':'CommunityName'})
merged.head()

nogeo_data = merged[['ZipCode', 'CaseCount', 'CommunityName']].fillna('Unknown')

import numpy as np
split0 = nogeo_data.loc[nogeo_data['CommunityName'] == 'San Diego']

## Split the all other remaining
split1, split2 = np.array_split(nogeo_data.loc[nogeo_data['CommunityName'] != 'San Diego'], 2)

# make groupby to create bokeh nested x range plots
sandiego = split0.groupby(by=['CommunityName', 'ZipCode'])
part1 = split1.groupby(by=['CommunityName', 'ZipCode'])
part2 = split2.groupby(by=['CommunityName', 'ZipCode'])

def create_plot(df):
    p = figure(plot_height=250, plot_width=800, x_range=df, toolbar_location=None,
               tooltips=[("CaseCount", "@CaseCount_mean"),
                         ("Community Name, ZipCode", "@CommunityName_ZipCode")]
              )

    p.vbar(x='CommunityName_ZipCode', top='CaseCount_mean', width=1, source=df,
           line_color="white" )

    p.y_range.start = 0
    p.x_range.range_padding = 0.05
    p.xgrid.grid_line_color = None
    p.yaxis.axis_label = "Case Count"
    p.xaxis.major_label_orientation = 22/28
    p.xaxis.group_label_orientation = 22/28
    p.xaxis.major_label_text_font_size = "8pt"
    p.xaxis.group_text_font_size = "10pt"
    p.title.text_font_size = "16pt"
    p.yaxis.axis_label_text_font_size = "16pt"
    p.xaxis.axis_label_text_font_size = "16pt"
    p.yaxis.axis_label_text_font_size = "16pt"
    p.outline_line_color = None
    p.x_range.group_padding = 1.0
    return p

bar_chart = Column( create_plot(sandiego), create_plot(part1), create_plot(part2))
# groupby are not sorted by bokeh. seems like a bug

Chloropleth Map of Covid 19 cases according to Zip Code in San Diego

# Convert merged data to json. Because there are some case counts from
# "unknown" they dont have any geo information.

merged_json = json.loads(merged.dropna().to_json())

# Convert to json to str like object because bokeh needs it in this form.
json_data = json.dumps(merged_json)

from bokeh.models import GeoJSONDataSource, LinearColorMapper, ColorBar, HoverTool
from bokeh.palettes import brewer

geosource = GeoJSONDataSource(geojson = json.dumps(merged_json))


#Define a sequential multi-hue color palette.
palette = brewer['YlOrRd'][8]

#Reverse color order
palette = palette[::-1]
#max(merged['CaseCount'])
#Instantiate LinearColorMapper that linearly maps numbers in a range, into a sequence of colors.
color_mapper = LinearColorMapper(palette = palette, low = 0,
                                 high = 80 )

tick_labels = {0:"0",
               10:"10",
               20:"20",
               30:"30",
               40:"40",
               50:"50",
               60:"60",
               70:"70",
               80:">90"
              }
#Create color bar.
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=8,
                     width = 500, height = 20,border_line_color=None,
                     location = (0,0),
                     orientation = 'horizontal',
                     major_label_overrides = tick_labels)

#Add hover tool
hover = HoverTool(tooltips = [ ('CaseCount','@CaseCount'),
                              ('Zip Code', '@ZipCode'),
                              ('Community Name', '@CommunityName')]
                 )

#Create figure object.
map_figure = figure(
    x_axis_location=None, y_axis_location=None,
           plot_height = 1000 , plot_width = 950,
           toolbar_location = None,
          tools = [hover])

map_figure.xgrid.grid_line_color = None
map_figure.ygrid.grid_line_color = None

#Add patch renderer to figure.
map_figure.patches('xs','ys', source = geosource,
          fill_color = {'field' :'CaseCount', 'transform' : color_mapper},
          line_color = 'black', line_width = 0.25, fill_alpha = 1)

map_figure.title.text_font_size = '16pt'
#Specify figure layout.
map_figure.add_layout(color_bar, 'above')


map_collage = Column( Div(text = title,  style={'font-size': '200%', 'color': 'blue'}),
                Div(text = f'Total number of cases: {total_count}',style={'font-size': '200%', 'color': 'red'}),
                    map_figure)

#show(map_figure)

collage = Column(map_collage, bar_chart)
show(collage)

# Generate standlone html documents with the collage of both plots

from bokeh.resources import CDN
from bokeh.embed import file_html

try:
    html1 = file_html(collage, CDN, 'Covid19 Cases in San Diego with Bokeh')
except Exception as e:
    print(e)


with open('Covid19_apr20.html','w') as f:
    f.write(html1)